wconf

Allows users to create weighted confusion matrices and accuracy metrics that help with the model selection process for classification problems, where distance from the correct category is important. The package includes several weighting schemes which can be parameterized, as well as custom configuration options. Furthermore, users can decide whether they wish to positively or negatively affect the accuracy score as a result of applying weights to the confusion matrix. Functions are included to calculate accuracy metrics for imbalanced data. Finally, 'wconf' integrates well with the 'caret' package, but it can also work standalone when provided data in matrix form. References: Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package" doi:10.18637/jss.v028.i05 Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization (and the mewto R package)" doi:10.2139/ssrn.3805911 Monahov, A. (2024) "Improved Accuracy Metrics for Classification with Imbalanced Data and Where Distance from the Truth Matters, with the wconf R Package" doi:10.2139/ssrn.4802336 Starovoitov, V., Golub, Y. (2020). New Function for Estimating Imbalanced Data Classification Results. Pattern Recognition and Image Analysis, 295–302 Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023) "A general framework for implementing distances for categorical variables" doi:10.48550/arXiv.2301.02190.

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